Agentic AI: From global hype to enterprise readiness

Time of India

2 days ago

In a world of accelerating digital decisions, enterprises are beginning to ask not just what AI can do, but what it should initiate on its own. That's where agentic AI enters the conversation.
Unlike traditional AI systems that passively respond to prompts, agentic AI systems are designed to act with intention, initiating tasks, adapting to context, and collaborating across systems to fulfill defined goals. In theory, this unlocks immense potential. In practice, especially within enterprise settings, agentic AI introduces a new set of questions around trust, control, and accountability.
The global narrative is evolving fast.
Yet when viewed through the lens of Indian enterprises, where priorities remain grounded in regulatory compliance, operational efficiency, and explainability, a different picture emerges.
Autonomy with purpose
The term 'agentic' borrows from cognitive science, referring to systems that can make decisions, pursue goals, and modify their behavior based on outcomes. Applied to AI, this translates into intelligent agents that move beyond generating insights to acting on them, navigating workflows, triggering processes, or interacting with other systems autonomously.
Crucially, these agents are not designed to replace humans but to work alongside them, handling high-volume, routine decisions while allowing humans to intervene in moments of nuance or consequence. This distinction is key to enterprise adoption. Across global markets,
agentic AI
is in an experimental phase. Tech leaders are piloting agents that support tasks like research synthesis, fraud triage, customer issue resolution, and even limited supply chain rebalancing.
The potential to reduce decision latency and extend human capacity is clear.
Deepak Ramanathan, Vice President, Global Technology Practice, SAS.
But early adopters are also uncovering hard boundaries. How do you calibrate autonomy when outcomes have regulatory impact? Can you explain decision paths in systems that learn and evolve? Who remains accountable when agents operate beyond visibility? These are not just technical questions but structural ones.
And they define how organizations will progress from prototypes to production.
In India, AI interest is surging but agentic AI remains a nascent concept. Organisations are still solidifying their data foundations, automating core processes, and building AI maturity across departments. The emphasis, rightly, is on value, clarity, and control. That's not to say agentic AI doesn't have a role. On the contrary, India's operational scale and appetite for leapfrogging legacy systems could make it fertile ground for agentic models, if they're designed with local realities in mind:
Systems must work across fragmented data environments.
AI agents must be transparent in how they operate.
Human oversight cannot be an afterthought—it must be embedded.
In essence, Indian enterprises don't just need smart agents—they need accountable collaborators to help address these key factors that hinge on the successful implementation of agentic AI.
The opportunity shift from assistants to agents
W
here agentic AI stands out is in its ability to orchestrate complexity across enterprise environments. Imagine intelligent agents that can analyze real-time fraud signals, detect anomalies, and trigger rule-based interventions without manual oversight. These agents can also monitor customer journeys, recognize patterns of drop-off, and autonomously adjust outreach strategies to improve engagement.
In compliance functions, they can assist teams by compiling audit trails or escalating potential policy breaches based on predefined thresholds.
These are not futuristic concepts but natural evolutions of current workflows. What's changing is the proactive role AI can now play. The real opportunity lies not just in automation, but in enabling calibrated and context-aware autonomy. For that, enterprises will need platforms that are not only technically capable but built with governance, transparency, and ethical alignment at their core.
The true value of agentic AI will come not from unchecked independence, but from intelligent systems designed to collaborate with humans—augmenting judgment, not replacing it. To achieve this, AI agents must be built with bounded autonomy, guided by three foundational principles.
First is decisioning– the use of hybrid intelligence that combines deterministic analytics with reasoning models to ensure decisions are both accurate and auditable.
Second is achieving the right human-AI balance, where agents are empowered to act independently only when tasks are well-defined and risk thresholds are acceptable, deferring to human intervention when context or complexity demands it. And third is governance, ensuring every action taken by an agent is aligned with data privacy policies, ethical standards, and industry-specific compliance frameworks.
The goal is not to build systems that replace people, but ones where autonomy operates in service of responsibility and trust becomes a design feature, not an afterthought.
Autonomy with accountability is the future
Agentic AI represents a bold frontier—but not one built on hype alone. Its success hinges on trust, governance, and the ability to embed AI into real-world decisions that matter. For Indian enterprises navigating transformation at scale, the message is clear: Don't chase autonomy for its own sake. Design it to reflect your values, amplify your decisions, and accelerate your goals. The next era of AI isn't just generative.
It's agentic. And it's here to work with you.